There are two main requirements for training UA models, 1) from the performance perspective, the embedding vectors must be highly separable [1]}, and 2) the training method must be privacy-preserving, i.e., the raw inputs or the embedding vector may not be shared with other entities participating in training. In the following, we present Federated User Authentication (FedUA) framework and describe its properties.
